An introduction to helpful forecasting methods for hotel revenue management

Pereira, Luìs Nobre

doi:10.1016/j.ijhm.2016.07.003

Cited by 63 publications

(55 citation statements)

References 29 publications

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“…The main advantages of the SdLy method are minimal data requirements and ease of implementation. Nonetheless, a recent study [28] has shown that, in a number of circumstances, the performance of SdLy forecasts is comparable to that of more sophisticated time series models over multi-step-ahead forecast horizons.…”

Section: Historical Booking Forecastsmentioning

confidence: 99%

“…A major shortcoming of SdLy forecasts is the use of a single data point, which may be subject to considerable noise. However, when SdLy is replaced by a simple average of occupancy levels on the same day of a few previous years, a substantial increase in forecast errors is possible [28]. A plausible explanation for these findings relates to the influence of special events that take place in the hotel itself, or in the destination.…”

Section: Historical Booking Forecastsmentioning

confidence: 99%

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Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability

Fiori

Foroni

2019

Sustainability

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In many tourism destinations, sustainability of the local economy leans on small and medium-sized hotels that are individually owned and operated by members of the community. Suffering from seasonality more than their big competitors, these hotels should undertake marketing initiatives to counteract wide demand fluctuations. Such initiatives are most effective if based on accurate occupancy forecasts, which must be performed at the individual hotel level. In this aim, the present paper suggests a demand forecasting approach adapted to specific features that characterize reservation data for small and medium-sized enterprises (SMEs) in the hospitality sector. The proposed framework integrates historical and advanced booking methods into a forecast combination with time-varying, performance-based weights. Whereas historical methods use only past observations about the number of guests recorded on a particular stay night to forecast future room occupancy (long-term perspective), advanced booking methods predict bookings-to-come based on partially accumulated data from reservations on hand (short-term perspective). In order to provide a possible solution to data sparsity issues that affect the application of advanced booking models to hospitality SMEs, a procedure that incorporates length-of-stay information directly into the reservation processing phase is also introduced. The methodology is tested on real time series of reservation data from three Italian hotels, located either in a city center (Milan) or in a typical destination for seasonal holidays (Lake Maggiore). Model parameters are calibrated on a training dataset and the accuracy of the occupancy forecasts is evaluated on a holdout sample. The results validate earlier findings about combinations of long-term and short-term forecasts and, in addition, show that using performance-based weights improves the quality of forecasts. Reducing the risk of large forecast failures, the proposed methodology can indeed have practical implications for the design and implementation of effective demand-side policies in hospitality SMEs. These policies are expected to provide a competitive advantage that can be crucial to the sustainability of small establishments in a context of growing global tourism.

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Section: Historical Booking Forecastsmentioning

confidence: 99%

Section: Historical Booking Forecastsmentioning

confidence: 99%

Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability

Fiori

Foroni

2019

Sustainability

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“…Rong et al [28], Xiang et al [29], Li et al [30], Yang et al [31], Guo et al [32], Versichele et al [33], Sun et al [34], Athanasopoulos et al, [35], Schwartz and Hiemstra [36], Yang et al [37], Tussyadiah and Wang [38], Pereira [39], Liu et al [40], Lim et al [41], Wu et al [42], Zhang and Zhang [43], and Chiu et al [44] have all introduced new "unconventional" variables while studying hospitality performance. The results are evidence that machine learning techniques are extremely powerful in enhancing the accuracy of analysis and prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hotel practitioners have used inventory planning and pricing as major inputs in DOI: 10.4236/tel.2018.89104 1625 Theoretical Economics Letters M. Al Shehhi, A. Karathanasopoulos their revenue management systems, which has led to successful hotel performance [39]. Lee [46] found while measuring hotel room rates that prices were affected by both internal (hotel-specific) and external (economic) factors.…”

Section: Research Framework and Proposed Hypothesesmentioning

confidence: 99%

Forecasting Hotel Prices in Selected Middle East and North Africa Region (MENA) Cities with New Forecasting Tools

Shehhi¹,

Karathanasopoulos²

2018

TEL

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The purpose of this paper is to understand the potential of traditional and non-traditional statistical techniques to predict dynamic hotel room prices. Four forecast models were employed: the simple moving average, the autoregressive integrated moving average (ARIMA), the radial basis function (RBF), and the support vector machine (SVM). This research is based on an empirical study of data obtained from the company Smith Travel Research (STR). The economic predictors were obtained from other reliable sources such as the World Bank and the World Tourism Organization. This study agreed with existing literature on the ability of machine learning to predict hotel room prices precisely. Given the complexity of the hotel industry, the effect of external economic predictors was tested in the model. The challenge lay in dealing with the mixed frequencies observed in the collected data. This research is designed to add an innovative approach to the existing literature on machine learning in the hotel industry in the Middle East and North Africa (MENA) region. Some of the machine learning techniques used in this study constitute a contribution to the research conducted in this region. This creates a bridge between many academic disciplines such as computer science, economics, and marketing. Small hotel operators should benefit from this research when setting strategies as well as in using the model to set their relative room prices.

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“…Researches made to date that consider seasonality topics in hotel industry were dominantly focused on challenges in revenue management domain due to business seasonality (e.g. Pereira, 2016;Weatherford, 2016), or on seasonality business research in hotel management and/or seasonality degree movement over time (e.g. De Santis, Ferrante and Vaccina, 2011;Duro, 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Seasonality Impact on the Business Performance of Global Chain Hotels

Vrkljan¹,

Barišić²,

Vrenc³

2019

Tourism in Southern and Eastern Europe

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Starting with the notion that seasonality is one of the greatest challenges for the most hotels and tourist destinations, and without an exception for the hotels in European Mediterranean destination, this paper aims to gain knowledge of the coherence between business performance of global chain hotels and degree of seasonality, and if business or leisure hotels are more successful from the aspect of seasonality degree. Business performance is approximated by the following indicators: accommodation occupancy, average daily room rate (ADR) and revenue per available room (RevPAR). Data was collected via questionnaire from 196 global chain hotels from four Mediterranean tourist destinations (Croatia, Italy, Spain and Turkey). The data were analyzed using descriptive, inferential and multivariate statistics. The study concludes that there is a difference in business performance of the global chain hotels depending on the degree of seasonality. As the seasonality degree is lower, the hotels' business performance is higher. It is found that targeting the leisure or business hotel type cannot make the influence on the more favorable degree of seasonality. The paper contributes to theoretical and practical understanding of seasonality impact on the business performance of global chain hotels.

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An introduction to helpful forecasting methods for hotel revenue management

Cited by 63 publications

References 29 publications

Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability

Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability

Forecasting Hotel Prices in Selected Middle East and North Africa Region (MENA) Cities with New Forecasting Tools

Analysis of Seasonality Impact on the Business Performance of Global Chain Hotels

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